Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome

Mendelian randomization as a cornerstone of causal inference for gut microbiota and related diseases from the perspective of bibliometrics

Gut microbiota, a special group of microbiotas in the human body, contributes to health in a way that can't be ignored. In recent years, Mendelian randomization, which is a widely used and successful method of analyzing causality, has been investigated for the relationship between the gut microbiota and related diseases. Unfortunately, there seems to be a shortage of systematic bibliometric analysis in this field. Therefore, this study aims to investigate the research progress of Mendelian randomization for gut microbiota through comprehensive bibliometric analysis. In this study, publications about Mendelian randomization for gut microbiota were gathered from 2013 to 2023, utilizing the Web of Science Core Collection as our literature source database. The search strategies were as follows: TS = (intestinal flora OR gut flora OR intestinal microflora OR gut microflora OR intestinal microbiota OR gut microbiota OR bowel microbiota OR bowel flora OR gut bacteria OR intestinal tract bacteria OR bowel bacteria OR gut metabolites OR gut microbiota) and TS = (Mendelian randomization). VOSviewer (version 1.6.18), CiteSpace (version 6.1.R1), Microsoft Excel 2021, and Scimago Graphica were employed for bibliometric and visualization analysis. According to research, from January 2013 to August 2023, 154 publications on Mendelian randomization for gut microbiota were written by 1053 authors hailing from 332 institutions across 31 countries and published in 86 journals. China had the highest number of publications, with 109. Frontiers in Microbiology is the most prolific journal, and Lei Zhang has published the highest number of significant articles. The most popular keywords were "Mendelian randomization," "gut microbiota," "instruments," "association," "causality," "gut microbiome," "risk," "bias," "genome-wide association," and "causal relationship." Moreover, the current research hotspots in this field focus on utilizing a 2-sample Mendelian randomization to investigate the relationship between gut microbiota and associated disorders. This research systematically reveals a comprehensive overview of the literature that has been published over the last 10 years about Mendelian randomization for gut microbiota. Moreover, the knowledge of key information in the field from a bibliometric perspective may greatly facilitate future research in the field.

Inflammatory proteins may mediate the causal relationship between gut microbiota and inflammatory bowel disease: A mediation and multivariable Mendelian randomization study

This research investigates the causal relationships among gut microbiota, inflammatory proteins, and inflammatory bowel disease (IBD), including crohn disease (CD) and ulcerative colitis (UC), and identifies the role of inflammatory proteins as potential mediators. Our study analyzed gut microbiome data from 13,266 samples collected by the MiBioGen alliance, along with inflammatory protein data from recent research by Zhao et al, and genetic data on CD and UC from the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). We used Mendelian randomization (MR) to explore the associations, complemented by replication, meta-analysis, and multivariable MR techniques for enhanced accuracy and robustness. Our analysis employed several statistical methods, including inverse-variance weighting, MR-Egger, and the weighted median method, ensuring comprehensive and precise evaluation. After MR analysis, replication and meta-analysis, we revealed significant associations between 11 types of gut microbiota and 17 inflammatory proteins were associated with CD and UC. Mediator MR analysis and multivariable MR analysis showed that in CD, the CD40L receptor mediated the causal effect of Defluviitaleaceae UCG-011 on CD (mediation ratio 8.3%), and the Hepatocyte growth factor mediated the causal effect of Odoribacter on CD (mediation ratio 18%). In UC, the C-C motif chemokine 4 mediated the causal effect of Ruminococcus2 on UC (mediation ratio 4%). This research demonstrates the interactions between specific gut microbiota, inflammatory proteins, and CD and UC. Furthermore, the CD40L receptor may mediate the relationship between Defluviitaleaceae UCG-011 and CD; the Hepatocyte growth factor may mediate the relationship between Odoribacter and CD; and the C-C motif chemokine 4 may mediate the relationship between Ruminococcus2 and UC. The identified associations and mediation effects offer insights into potential therapeutic approaches targeting the gut microbiome for managing CD and UC.

Identification of Causal Relationships between Gut Microbiota and Influenza a Virus Infection in Chinese by Mendelian Randomization

Numerous studies have reported a correlation between gut microbiota and influenza A virus (IAV) infection and disease severity. However, the causal relationship between these factors remains inadequately explored. This investigation aimed to assess the influence of gut microbiota on susceptibility to human infection with H7N9 avian IAV and the severity of influenza A (H1N1)pdm09 infection. A two-sample Mendelian randomization analysis was conducted, integrating our in-house genome-wide association study (GWAS) on H7N9 susceptibility and H1N1pdm09 severity with a metagenomics GWAS dataset from a Chinese population. Twelve and fifteen gut microbiotas were causally associated with H7N9 susceptibility or H1N1pdm09 severity, separately. Notably, Clostridium hylemonae and Faecalibacterium prausnitzii were negative associated with H7N9 susceptibility and H1N1pdm09 severity, respectively. Moreover, Streptococcus peroris and Streptococcus sanguinis were associated with H7N9 susceptibility, while Streptococcus parasanguini and Streptococcus suis were correlated with H1N1pdm09 severity. These results provide novel insights into the interplay between gut microbiota and IAV pathogenesis as well as new clues for mechanism research regarding therapeutic interventions or IAV infections. Future studies should concentrate on clarifying the regulatory mechanisms of gut microbiota and developing efficacious approaches to reduce the incidence of IAV infections, which could improve strategy for preventing and treating IAV infection worldwide.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Gut microbiota and metabolites signatures of clinical response in anti-PD-1/PD-L1 based immunotherapy of biliary tract cancer

BACKGROUND

Accumulating evidence suggests that the gut microbiota and metabolites can modulate tumor responses to immunotherapy; however, limited data has been reported on biliary tract cancer (BTC). This study used metagenomics and metabolomics to identify characteristics of the gut microbiome and metabolites in immunotherapy-treated BTC and their potential as prognostic and predictive biomarkers.

METHODS

This prospective cohort study enrolled 88 patients with BTC who received PD-1/PD-L1 inhibitors from November 2018 to May 2022. The microbiota and metabolites significantly enriched in different immunotherapy response groups were identified through metagenomics and LC-MS/MS. Associations between microbiota and metabolites, microbiota and clinical factors, and metabolites and clinical factors were explored.

RESULTS

Significantly different bacteria and their metabolites were both identified in the durable clinical benefit (DCB) and non-durable clinical benefit (NDB) groups. Of these, 20 bacteria and two metabolites were significantly associated with survival. Alistipes were positively correlated with survival, while Bacilli, Lactobacillales, and Pyrrolidine were negatively correlated with survival. Predictive models based on six bacteria, four metabolites, and the combination of three bacteria and two metabolites could all discriminated between patients in the DCB and NDB groups with high accuracy. Beta diversity between two groups was significantly different, and the composition varied with differences in the use of immunotherapy.

CONCLUSIONS

Patients with BTC receiving immunotherapy have specific alterations in the interactions between microbiota and metabolites. These findings suggest that gut microbiota and metabolites are potential prognostic and predictive biomarkers for clinical outcomes of anti-PD-1/PD-L1-treated BTC.

Investigating gut microbiota-blood and urine metabolite correlations in early sepsis-induced acute kidney injury: insights from targeted KEGG analyses

Background

The interplay between gut microbiota and metabolites in the early stages of sepsis-induced acute kidney injury (SA-AKI) is not yet clearly understood. This study explores the characteristics and interactions of gut microbiota, and blood and urinary metabolites in patients with SA-AKI.

Methods

Utilizing a prospective observational approach, we conducted comparative analyses of gut microbiota and metabolites via metabolomics and metagenomics in individuals diagnosed with SA-AKI compared to those without AKI (NCT06197828). Pearson correlations were used to identify associations between microbiota, metabolites, and clinical indicators. The Comprehensive Antibiotic Resistance Database was employed to detect antibiotic resistance genes (ARGs), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways informed on metabolic processes and microbial resistance patterns.

Results

Our study included analysis of four patients with SA-AKI and five without AKI. Significant disparities in bacterial composition were observed, illustrated by diversity indices (Shannon index: 2.0 ± 0.4 vs. 1.4 ± 0.6, P = 0.230; Simpson index: 0.8 ± 0.1 vs. 0.6 ± 0.2, P = 0.494) between the SA-AKI group and the non-AKI group. N6, N6, N6-Trimethyl-L-lysine was detected in both blood and urine metabolites, and also showed significant correlations with specific gut microbiota (Campylobacter hominis and Bacteroides caccae, R > 0, P < 0.05). Both blood and urine metabolites were enriched in the lysine degradation pathway. We also identified the citrate cycle (TCA cycle) as a KEGG pathway enriched in sets of differentially expressed ARGs in the gut microbiota, which exhibits an association with lysine degradation.

Conclusions

Significant differences in gut microbiota and metabolites were observed between the SA-AKI and non-AKI groups, uncovering potential biomarkers and metabolic changes linked to SA-AKI. The lysine degradation pathway may serve as a crucial link connecting gut microbiota and metabolites.

The role of circulating metabolites and gut microbiome in hypertrophic scar: a two-sample Mendelian randomization study

Hypertrophic scarring is a fibro-proliferative disorder caused by abnormal cutaneous wound healing. Circulating metabolites and the gut microbiome may be involved in the formation of these scars, but high-quality evidence of causality is lacking. To assess whether circulating metabolites and the gut microbiome contain genetically predicted modifiable risk factors for hypertrophic scar formation. Two-sample Mendelian randomization (MR) was performed using MR-Egger, inverse-variance weighting (IVW), Mendelian Randomization Pleiotropy RESidual Sum and Outlier, maximum likelihood, and weighted median methods. Based on the genome-wide significance level, genetically predicted uridine (P = 0.015, odds ratio [OR] = 1903.514, 95% confidence interval [CI] 4.280-846,616.433) and isovalerylcarnitine (P = 0.039, OR = 7.765, 95% CI 1.106-54.512) were positively correlated with hypertrophic scar risk, while N-acetylalanine (P = 0.013, OR = 7.98E-10, 95% CI 5.19E-17-0.012) and glycochenodeoxycholate (P = 0.021, OR = 0.021 95% CI 0.003-0.628) were negatively correlated. Gastranaerophilales and two unknown gut microbe species (P = 0.031, OR = 0.378, 95% CI 0.156-0.914) were associated with an decreased risk of hypertrophic scarring. Circulating metabolites and gut microbiome components may have either positive or negative causal effects on hypertrophic scar formation. The study provides new insights into strategies for diagnosing and limiting hypertrophic scarring.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Unraveling the causal relationships between depression and brain structural imaging phenotypes: A bidirectional Mendelian Randomization study

BACKGROUND

Previous studies have revealed structural brain abnormalities in individuals with depression, but the causal relationship between depression and brain structure remains unclear.

METHODS

A genetic correlation analysis was conducted using summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 1,265 brain structural imaging-derived phenotypes (IDPs, N = 33,224). Subsequently, a bidirectional two-sample Mendelian Randomization (MR) approach was employed to explore the causal relationships between depression and the IDPs that showed genetic correlations with depression. The main MR results were obtained using the inverse variance weighted (IVW) method, and other MR methods were further employed to ensure the reliability of the findings.

RESULTS

Ninety structural IDPs were identified as being genetically correlated with depression and were included in the MR analyses. The IVW MR results indicated that reductions in the volume of several brain regions, including the bilateral subcallosal cortex, right medial orbitofrontal cortex, and right middle-posterior part of the cingulate cortex, were causally linked to an increased risk of depression. Additionally, decreases in surface area of the right middle temporal visual area, right middle temporal cortex, right inferior temporal cortex, and right middle-posterior part of the cingulate cortex were causally associated with a heightened risk of depression. Validation and sensitivity analyses supported the robustness of these findings. However, no evidence was found for a causal effect of depression on structural IDPs.

CONCLUSIONS

Our findings reveal the causal influence of specific brain structures on depression, providing evidence to consider brain structural changes in the etiology and treatment of depression.

Causal relationships of gut microbiota, plasma metabolites, and metabolite ratios with diffuse large B-cell lymphoma: a Mendelian randomization study

Background

Recent studies have revealed changes in microbiota constitution and metabolites associated with tumor progression, however, no causal relation between microbiota or metabolites and diffuse large B-cell lymphoma (DLBCL) has yet been reported.

Methods

We download a microbiota dataset from the MiBioGen study, a metabolites dataset from the Canadian Longitudinal Study on Aging (CLSA) study, and a DLBCL dataset from Integrative Epidemiology Unit Open genome-wide association study (GWAS) project. Mendelian randomization (MR) analysis was conducted using the R packages, TwoSampleMR and MR-PRESSO. Five MR methods were used: MR-Egger, inverse variance weighting (IVW), weighted median, simple mode, and weighted mode. Reverse MR analyses were also conducted to explore the causal effects of DLBCL on the microbiome, metabolites, and metabolite ratios. Pleiotropy was evaluated by MR Egger regression and MR-PRESSO global analyses, heterogeneity was assessed by Cochran's Q-test, and stability analyzed using the leave-one-out method.

Results

119 microorganisms, 1,091 plasma metabolite, and 309 metabolite ratios were analyzed. According to IVW analysis, five microorganisms were associated with risk of DLBCL. The genera Terrisporobacter (OR: 3.431, p = 0.049) andgenera Oscillibacter (OR: 2.406, p = 0.029) were associated with higher risk of DLBCL. Further, 27 plasma metabolites were identified as having a significant causal relationships with DLBCL, among which citrate levels had the most significant protective causal effect against DLBCL (p = 0.006), while glycosyl-N-tricosanoyl-sphingadienine levels was related to higher risk of DLBCL (p = 0.003). In addition, we identified 19 metabolite ratios with significant causal relationships to DLBCL, of which taurine/glutamate ratio had the most significant protective causal effect (p = 0.005), while the phosphoethanolamine/choline ratio was related to higher risk of DLBCL (p = 0.009). Reverse MR analysis did not reveal any significant causal influence of DLBCL on the above microbiota, metabolites, and metabolite ratios (p > 0.05). Sensitivity analyses revealed no significant heterogeneity or pleiotropy (p > 0.05).

Conclusion

We present the first elucidation of the causal influence of microbiota and metabolites on DLBCL using MR methods, providing novel insights for potential targeting of specific microbiota or metabolites to prevent, assist in diagnosis, and treat DLBCL.

Causal relationships between gut microbiota and lymphoma: a bidirectional Mendelian randomization study

Background

Multiple studies have suggested a possible connection between the gut microbiota and the development of lymphoma, though the exact nature of this relationship remains unclear. This study aimed to explore whether a causal association exists between gut microbiota and lymphoma.

Methods

A bidirectional two-sample Mendelian randomization (MR) approach was conducted to investigate potential causal effects between gut microbiota and various lymphoma subtypes. The primary method employed for MR analysis was inverse variance weighted (IVW), supplemented by additional methods including MR-Egger, weighted median, and weighted mode approaches. The Cochrane Q test, MR-PRESSO global test and MR-Egger intercept test were performed to assess pleiotropy and heterogeneity. Furthermore, a reverse MR analysis was performed to explore potential reverse causal effect.

Results

The primary MR analysis identified 36 causal relationships between genetic liabilities in gut microbiota and different lymphoma subtypes. Neither the MR-PRESSO test nor the MR-Egger regression detected any pleiotropy, and Cochran's Q test indicated no significant heterogeneity.

Conclusions

Our MR analysis revealed substantial causal associations between gut microbiota and lymphoma, offering new insights into lymphoma prevention and management microbiota.

Causal relationship between immune cells and atrial fibrillation: A Mendelian randomization study

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, with recent research indicating a correlation between immune system characteristics and the development of AF. However, it remains uncertain whether the immunological response is the primary underlying component or a secondary consequence of AF. Initially, we investigated the effect of immune cells on AF by performing forward Mendelian randomization (MR) analyses with immune cells as the exposure variable and their associated genetic variants as instrumental variables. Subsequently, we performed reverse MR analyses with AF as the exposure variable and immune cells as the outcome variable to exclude the interference of reverse causality, to distinguish between primary and secondary effects, and to further elucidate the causal relationship between the immune system and AF. We discovered that membrane proteins on specific immune cells, such as CD25 on memory B cells-which functions as a part of the interleukin-2 receptor-may be risk factors for AF development, with odds ratios of 1.0233 (95% confidence interval: 1.0012-1.0458, P = .0383). In addition, certain immune cell counts, such as the CD4 regulatory T cell Absolute Count, play a protective factor in the development of AF (odds ratio: 0.9513, 95% confidence interval: 0.9165-0.9874; P = .0086). More detailed results are elaborated in the main text. Our MR study has yielded evidence that substantiates a genetically inferred causal association between the immune system and AF. Identifying the risk factors associated with AF is vital to facilitate the development of innovative pharmaceutical treatments.

Association of gut microbiota with cerebral cortical thickness: A Mendelian randomization study

BACKGROUND

The causal relationship between gut microbiota and cerebral cortex development remains unclear. We aimed to scrutinize the plausible causal impact of gut microbiota on cortical thickness via Mendelian randomization (MR) study.

METHODS

Genome-wide association study (GWAS) data for 196 gut microbiota phenotypes (N = 18,340) were obtained as exposures, and GWAS data for cortical thickness-related traits (N = 51,665) were selected as outcomes. Inverse variance weighted was used as the main estimate method. A series of sensitivity analyses was used to test the robustness of the estimates including Cochran's Q test, MR-Egger intercept analysis, Steiger filtering, scatter plot funnel plot and leave-one-out analysis.

RESULTS

Genetic prediction of high Bacillales (β = 0.005, P = 0.032) and Lactobacillales (β = 0.010, P = 0.012) abundance was associated with a potential increase in global cortical thickness. For specific functional brain subdivisions, genetically predicted order Lactobacillales would potentially increase the thickness of the fusiform (β = 0.014, P = 0.016) and supramarginal (β = 0.017, P = 0.003). Meanwhile, order Bacillales would increase the thickness of fusiform (β = 0.007, P = 0.039), insula (β = 0.011, P = 0.003), rostralanteriorcingulate (β = 0.014, P = 0.002) and supramarginal (β = 0.006, P = 0.043). No significant estimates of heterogeneity or pleiotropy were found.

CONCLUSIONS

Through MR studies, we discovered genetic prediction of the Lactobacillales and Bacillales orders potentially linked to cortical thickness, affirming gut microbiota may enhance brain structure. Genetically predicted supramarginal and fusiform may be potential targets.

Diet Mediate the Impact of Host Habitat on Gut Microbiome and Influence Clinical Indexes by Modulating Gut Microbes and Serum Metabolites

The impact of external factors on the human gut microbiota and how gut microbes contribute to human health is an intriguing question. Here, the gut microbiome of 3,224 individuals (496 with serum metabolome) with 109 variables is studied. Multiple analyses reveal that geographic factors explain the greatest variance of the gut microbiome and the similarity of individuals' gut microbiome is negatively correlated with their geographic distance. Main food components are the most important factors that mediate the impact of host habitats on the gut microbiome. Diet and gut microbes collaboratively contribute to the variation of serum metabolites, and correlate to the increase or decrease of certain clinical indexes. Specifically, systolic blood pressure is lowered by vegetable oil through increasing the abundance of Blautia and reducing the serum level of 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1), but it is reduced by fruit intake through increasing the serum level of Blautia improved threonate. Besides, aging-related clinical indexes are also closely correlated with the variation of gut microbes and serum metabolites. In this study, the linkages of geographic locations, diet, the gut microbiome, serum metabolites, and physiological indexes in a Chinese population are characterized. It is proved again that gut microbes and their metabolites are important media for external factors to affect human health.

Genetically proxied intestinal microbiota and risk of erectile dysfunction

BACKGROUND

The interaction between intestinal microbiota and erectile dysfunction (ED) is less investigated. This study was performed to explore the association between intestinal microbiota and ED.

METHODS

In this two-sample Mendelian randomization (MR) study, genetic variants of gut microbiota were obtained from MiBioGen consortium containing 18,340 individuals. Six methods including inverse variance weighting (IVW), MR-Egger, weighted median, maximum likelihood, MR robust adjusted profile score, and MR pleiotropy residual sum and outlier were used to investigate the causal links between intestinal microbiota and ED. Furthermore, reverse MR analysis was performed to exclude the causal impact of ED on gut microbiota.

RESULTS

As revealed by the IVW estimator, the risks of ED were raised by genetically proxied Lachnospiraceae (OR: 1.27), Lachnospiraceae NC2004 group (OR: 1.17), Oscillibacter (OR: 1.20), Senegalimassilia (OR: 1.32) (All P < 0.05) and Tyzzerella-3 (OR: 1.14, P < 0.05). It was observed that Ruminococcaceae UCG013 exerted protective effect against ED (OR: 0.77, P < 0.05). These results were consistent with other estimators in sensitivity analyses. In reverse MR analyses, genetic liability to ED did not alter the abundances of Lachnospiraceae, Lachnospiraceae NC2004 group, Oscillibacter, Senegalimassilia, Tyzzerella-3, and Ruminococcaceae UCG013 (All P > 0.05). No heterogeneity and pleiotropy were detected by Cochran's Q-test, MR-Egger, and global test (All P > 0.05).

CONCLUSIONS

This study provided novel evidence that genetically proxied Lachnospiraceae, Lachnospiraceae NC2004 group, Oscillibacter, Senegalimassilia, Tyzzerella-3, and Ruminococcaceae UCG013 had potentially causal effects on ED. Further studies are needed to clarify the biological mechanisms linking intestinal microbiota to ED.

Exploring the link between gut microbiota and alopecia areata: a two-sample Mendelian randomization analysis

BACKGROUND

While observational studies have suggested a link between gut microbiota diversity and alopecia areata (AA), the causal relationship remains unclear.

METHODS

We leveraged data from the MiBioGen and FinnGen consortiums' Genome-wide association studies (GWAS) encompassing gut microbiota (n = 13,266) and AA (n = 211,428) datasets. A comprehensive Mendelian randomization (MR) and reverse MR approach were employed, utilizing five statistical methods to evaluate causality. Sensitivity analyses were also conducted to corroborate the MR results.

RESULTS

Inverse variance weighted (IVW) analysis indicated a protective effect against AA from Butyricimonas (OR = 0.37, 95% CI: 0.18-0.77, P = 0.01), Enterorhabdus (OR = 0.40, 95% CI: 0.16-0.95, P = 0.04), Eubacterium (xylanophilum group) (OR = 0.36, 95% CI: 0.15-0.84, P = 0.02), and Phascolarctobacterium (OR = 0.37, 95% CI: 0.15-0.91, P = 0.03), while Ruminococcaceae UCG003 posed as a risk factor (OR = 2.79, 95% CI: 1.27-6.14, P = 0.01). Reverse MR showed no significant causal link between AA and gut microbiota, with no significant heterogeneity or horizontal pleiotropy.

CONCLUSIONS

Our analysis suggests probable causality between certain gut microbiota and AA, shedding light on its pathogenesis and potential intervention strategies.

Causal relationships between gut microbrome and digestive system diseases: A two-sample Mendelian randomization study

Growing evidences of recent studies have shown that gut microbrome are causally related to digestive system diseases (DSDs). However, causal relationships between the gut microbiota and the risk of DSDs still remain unclear. We utilized identified gut microbiota based on class, family, genus, order and phylum information and digestive system diseases genome-wide association study (GWAS) dataset for two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method was used to evaluate causal relationships between gut microbiota and 7 DSDs, including chronic gastritis, colorectal cancer, Crohn's disease, gastric cancer, gastric ulcer, irritable bowel syndrome and esophageal cancer. Finally, we verified the robustness of MR results based on heterogeneity and pleiotropy analysis. We discovered 15 causal associations with genetic liabilities in the gut microbiota and DSDs, such as genus Victivallis, genus RuminococcaceaeUCG005, genus Ruminococcusgauvreauiigroup, genus Oxalobacter and so on. Our MR analysis revealed that the gut microbiota is causally associated with DSDs. Further researches of the gut microbiota and the pathogenesis of DSDs are still significant and provide new methods for the prevention and treatment of DSDs.

Blood lipids mediate the effects of gut microbiome on endometriosis: a mendelian randomization study

BACKGROUND

There is evidence for an association between the gut microbiome and endometriosis. However, their causal relationship and the mediating role of lipid metabolism remain unclear.

METHODS

Using genome-wide association study (GWAS) data, we conducted a bidirectional Mendelian randomization (MR) analysis to investigate the causal relationships between gut microbiome and endometriosis. The inverse variance weighted (IVW) method was used as the primary model, with other MR models used for comparison. Sensitivity analysis based on different statistical assumptions was used to evaluate whether the results were robust. A two-step MR analysis was further conducted to explore the mediating effects of lipids, by integrating univariable MR and the multivariate MR method based on the Bayesian model averaging method (MR-BMA).

RESULTS

We identified four possible intestinal bacteria genera associated with the risk of endometriosis through the IVW method, including Eubacterium ruminantium group (odds ratio [OR] = 0.881, 95% CI: 0.795-0.976, P = 0.015), Anaerotruncus (OR = 1.252, 95% CI: 1.028-1.525, P = 0.025), Olsenella (OR = 1.110, 95% CI: 1.007-1.223, P = 0.036), and Oscillospira (OR = 1.215, 95% CI: 1.014-1.456, P = 0.035). The further two-step MR analysis identified that the effect of Olsenella on endometriosis was mediated by triglycerides (proportion mediated: 3.3%; 95% CI = 1.5-5.1%).

CONCLUSION

This MR study found evidence for specific gut microbiomes associated with the risk of endometriosis, which might partially be mediated by triglycerides.

Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and in vitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health.

Exploring the causal relationship between periodontitis and gut microbiome: Unveiling the oral-gut and gut-oral axes through bidirectional Mendelian randomization

AIM

This Mendelian randomization (MR) study was performed to explore the potential bidirectional causal relationship between the gut microbiome (GM) and periodontitis.

MATERIALS AND METHODS

We used genetic instruments from the genome-wide association study of European descent for periodontitis from the GeneLifestyle Interactions in Dental Endpoints (GLIDE) consortium (17,353 cases and 28,210 controls) and the FinnGen consortium (4434 cases and 259,234 controls) to investigate the causal relationship with GM (the MiBioGen consortium, 18,340 samples), and vice versa. Several MR techniques, which include inverse variance weighting (IVW), MR-Egger, weighted median, simple mode and weighted mode approaches, were employed to investigate the causal relationship between the exposures and the outcomes. Cochran's Q-test was performed to detect heterogeneity. The MR-Egger regression intercept and MR pleiotropy residual sum and outlier test (MR-PRESSO) were conducted to test potential horizontal pleiotropy. Leave-one-out sensitivity analyses were used to assess the stabilities of single nucleotide polymorphisms (SNPs). Finally, the IVW results from the two databases were analysed using meta-analysis.

RESULTS

We confirmed three potential causal relationships between GM taxa and periodontitis at the genus level. Among them, the genera Alistipes and Holdemanella were genetically associated with an increased risk of periodontitis. In reverse, periodontitis may lead to a decreased abundance of the genus Ruminococcaceae UCG014.

CONCLUSIONS

The demonstration of a causal link between GM and periodontitis provides compelling evidence, highlighting the interconnectivity and interdependence of the gut-oral and oral-gut axes.

The unique biodegradation pathway of benzo[a]pyrene in moderately halophilic Pontibacillus chungwhensis HN14

Benzo[a]pyrene (BaP), as the typical representative of polycyclic aromatic hydrocarbons (PAHs), is a serious hazard to human health and natural environments. Though the study of microbial degradation of PAHs has persisted for decades, the degradation pathway of BaP is still unclear. Previously, Pontibacillus chungwhensis HN14 was isolated from high salinity environment exhibiting a high BaP degradation ability. Here, based on the intermediates identified, BaP was found to be transformed to 4,5-epoxide-BaP, BaP-trans-4,5-dihydrodiol, 1,2-dihydroxy-phenanthrene, 2-carboxy-1-naphthol, and 4,5-dimethoxybenzo[a]pyrene by the strain HN14. Furthermore, functional genes involved in degradation of BaP were identified using genome and transcriptome data. Heterogeneous co-expression of monooxygenase CYP102(HN14) and epoxide hydrolase EH(HN14) suggested that CYP102(HN14) could transform BaP to 4,5-epoxide-BaP, which was further transformed to BaP-trans-4,5-dihydrodiol by EH(HN14). Moreover, gene cyp102(HN14) knockout was performed using CRISPR/Cas9 gene-editing system which confirmed that CYP102(HN14) play a key role in the initial conversion of BaP. Finally, a novel BaP degradation pathway was constructed in bacteria, which showed BaP could be converted into chrysene, phenanthrene, naphthalene pathways for the first time. These findings enhanced our understanding of microbial degradation process for BaP and suggested the potential of using P. chungwhensis HN14 for bioremediation in PAH-contaminated environments.

Causal link between gut microbiome and schizophrenia: a Mendelian randomization study

OBJECTIVE

Some observational studies have shown that gut microbiome is significantly changed in patients with schizophrenia. We aim to identify the genetic causal link between gut microbiome and schizophrenia.

METHODS

A two-sample Mendelian randomization (MR) study was used to evaluate the causal link between gut microbiome and schizophrenia with 28 gut microbiome-associated genetic instrumental variants chosen from recent MR reports and the largest schizophrenia genome-wide association studies (8-Apr-22 release).

RESULTS

Inverse variance weighted method showed that genetically increased Bacteroidales_S24-7 (per SD) resulted in increased risk of schizophrenia (OR = 1.110, 95% CI: [1.012-1.217], P  = 0.027). Similarly, genetically increased Prevotellaceae promoted schizophrenia risk (OR = 1.124, 95% CI: [1.030-1.228], P  = 0.009). However, genetically increased Lachnospiraceae reduced schizophrenia risk (OR = 0.878, 95% CI: [0.785-0.983], P  = 0.023). In addition, schizophrenia risk was also suppressed by genetically increased Lactobacillaceae (OR = 0.878, 95% CI: [0.776-0.994], P  = 0.040) and Verrucomicrobiaceae (OR = 0.860, 95% CI: [0.749-0.987], P  = 0.032). Finally, we did not find any significant results in the causal association of other 23 gut microbiome with schizophrenia.

CONCLUSION

Our analysis suggests that genetically increased Bacteroidales_S24-7 and Prevotellaceae promotes schizophrenia risk, whereas genetically increased Lachnospiraceae, Lactobacillaceae, and Verrucomicrobiaceae reduces schizophrenia risk. Thus, regulation of the disturbed intestinal microbiota may represent a new therapeutic strategy for patients with schizophrenia.

Assessing the causal relationships of gut microbial genera with hyperuricemia and gout using two-sample Mendelian randomization

BACKGROUND AND AIMS

The causal relationship between gut microbiota and gout and hyperuricemia (HUA) has not been clarified. The objective of this research was to evaluate the potential causal effects of gut microbiota on HUA and gout using a two-sample Mendelian randomization (MR) approach.

METHODS AND RESULTS

Genetic instruments were selected using summary statistics from genome-wide association studies (GWASs) comprising a substantial number of individuals, including 18,473 participants for gut microbiome, 288,649 for serum urate (SU), and 763,813 for gout. Two-sample MR analyses were performed to determine the possible causal associations of gut microbial genera with the risk of HUA and gout using the inverse-variance weighted (IVW) method, and robustness of the results was confirmed by several sensitivity analyses. A reverse MR analysis was conducted on the bacterial taxa that were identified in forward MR analysis. Based on the results of MR analyses, Escherichia-Shigella (OR = 1.05; 95% CI, 1.01-1.08; P = 0.009) exhibited a positive association with SU levels, while Lachnospiraceae NC2004 group (OR = 0.95; 95% CI, 0.92-0.98; P = 0.001) and Family XIII AD3011 group (OR = 0.94; 95% CI, 0.90-0.99; P = 0.015) were associated with a reduced HUA risk. Moreover, Coprococcus 3 (OR = 1.17, 95% CI: 1.01-1.34, P = 0.031) was causally associated with a higher gout risk. In reverse MR analysis, no causal relationships were identified between these bacterial genera and HUA or gout.

CONCLUSION

This study provides evidence for a causal association between gut microbial genera and HUA or gout, and further investigations of the underlying mechanism are warranted.

Pancreatitis affects gut microbiota via metabolites and inflammatory cytokines: an exploratory two-step Mendelian randomisation study

Previous studies have observed relationships between pancreatitis and gut microbiota; however, specific changes in gut microbiota abundance and underlying mechanisms in pancreatitis remain unknown. Metabolites are important for gut microbiota to fulfil their biological functions, and changes in the metabolic and immune environments are closely linked to changes in microbiota abundance. We aimed to clarify the mechanisms of gut-pancreas interactions and explore the possible role of metabolites and the immune system. To this end, we conducted two-sample Mendelian randomisation (MR) analysis to evaluate the casual links between four different types of pancreatitis and gut microbiota, metabolites, and inflammatory cytokines. A two-step MR analysis was conducted to further evaluate the probable mediating pathways involving metabolites and inflammatory cytokines in the causal relationship between pancreatitis and gut microbiota. In total, six potential mediators were identified in the causal relationship between pancreatitis and gut microbiota. Nineteen species of gut microbiota and seven inflammatory cytokines were genetically associated with the four types of pancreatitis. Metabolites involved in glucose and amino acid metabolisms were genetically associated with chronic pancreatitis, and those involved in lipid metabolism were genetically associated with acute pancreatitis. Our study identified alterations in the gut microbiota, metabolites, and inflammatory cytokines in pancreatitis at the genetic level and found six potential mediators of the pancreas-gut axis, which may provide insights into the precise diagnosis of pancreatitis and treatment interventions for gut microbiota to prevent the exacerbation of pancreatitis. Future studies could elucidate the mechanism underlying the association between pancreatitis and the gut microbiota.

Univariable and multivariable Mendelian randomization study identified the key role of gut microbiota in immunotherapeutic toxicity

BACKGROUND

In cancer patients receiving immune checkpoint inhibitors (ICIs), there is emerging evidence suggesting a correlation between gut microbiota and immune-related adverse events (irAEs). However, the exact roles of gut microbiota and the causal associations are yet to be clarified.

METHODS

To investigate this, we first conducted a univariable bi-directional two-sample Mendelian randomization (MR) analysis. Instrumental variables (IVs) for gut microbiota were retrieved from the MiBioGen consortium (18,340 participants). GWAS summary data for irAEs were gathered from an ICIs-treated cohort with 1,751 cancer patients. Various MR analysis methods, including inverse variance weighted (IVW), MR PRESSO, maximum likelihood (ML), weighted median, weighted mode, and cML-MA-BIC, were used. Furthermore, multivariable MR (MVMR) analysis was performed to account for possible influencing instrumental variables.

RESULTS

Our analysis identified fourteen gut bacterial taxa that were causally associated with irAEs. Notably, Lachnospiraceae was strongly associated with an increased risk of both high-grade and all-grade irAEs, even after accounting for the effect of BMI in the MVMR analysis. Akkermansia, Verrucomicrobiaceae, and Anaerostipes were found to exert protective roles in high-grade irAEs. However, Ruminiclostridium6, Coprococcus3, Collinsella, and Eubacterium (fissicatena group) were associated with a higher risk of developing high-grade irAEs. RuminococcaceaeUCG004, and DefluviitaleaceaeUCG011 were protective against all-grade irAEs, whereas Porphyromonadaceae, Roseburia, Eubacterium (brachy group), and Peptococcus were associated with an increased risk of all-grade irAEs.

CONCLUSIONS

Our analysis highlights a strong causal association between Lachnospiraceae and irAEs, along with some other gut microbial taxa. These findings provide potential modifiable targets for managing irAEs and warrant further investigation.

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4-40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

Inflammatory cytokines and their potential role in Sjogren's syndrome risk: insights from a mendelian randomization study

AIM

This study aimed to investigate the causal impact of inflammatory cytokines on Sjogren's Syndrome (SS) and to identify potential biomarkers for SS clinical management using Mendelian Randomization (MR).

MATERIALS AND METHODS

Leveraging GWAS summary data of inflammatory cytokines and SS, we executed the first two-sample MR analysis. Genetic variants from prior GWASs associated with circulating inflammatory cytokines served as instrumental variables (IVs). Data regarding cytokines were analyzed using the Olink Target-96 Inflammation panel, synthesizing data from 14,824 participants. GWAS summary statistics for SS were procured from the UK Biobank, focusing on samples of European ancestry. To discern the causal relationship between inflammatory cytokines and SS, several MR methodologies, including inverse variance weighted (IVW) and MR-Egger regression, were applied.

RESULTS

After rigorous IV quality control, 91 cytokines were incorporated into the MR analysis. The IVW analysis identified 8 cytokines with a positive association to SS: Axin-1 (OR 2.56, 95% CI 1.07-6.10), T-cell surface glycoprotein CD5 (OR 1.81, 95% CI 1.08-3.02), CUDP1 (OR 1.61, 95% CI 1.00-2.58), CXCL10 (OR 1.92, 95% CI 1.25-2.95), IL-4 (OR 2.18, 95% CI 1.22-3.91), IL-7 (OR 2.35, 95% CI 1.27-4.33), MCP-2 (OR 1.27, 95% CI 1.05-1.54), and TNFRSF9 (OR 1.83, 95% CI 1.03-3.24), suggesting their potential in increasing SS risk.

CONCLUSION

Our study conducted through MR, identified various inflammatory cytokines associated with SS risk, validating some previous research results and offering some new potential biomarkers for SS. However, these findings necessitate further research for validation and exploration of their precise role in the onset and progression of SS.

Association between Gut Microbiota and Biological Aging: A Two-Sample Mendelian Randomization Study

Recent observational studies revealed an association between gut microbiota and aging, but whether gut microbiota are causally associated with the aging process remains unknown. We used a two-sample Mendelian randomization approach to investigate the causal association between gut microbiota and biological age acceleration using the largest available gut microbiota GWAS summary data from the MiBioGen consortium and GWAS data on biological age acceleration. We further conducted sensitivity analysis using MR-PRESSO, MR-Egger regression, Cochran Q test, and reverse MR analysis. Streptococcus (IVW, β = 0.16, p = 0.0001) was causally associated with Bioage acceleration. Eubacterium (rectale group) (IVW, β = 0.20, p = 0.0190), Sellimonas (IVW, β = 0.06, p = 0.019), and Lachnospira (IVW, β = -0.18, p = 0.01) were suggestive of causal associations with Bioage acceleration, with the latter being protective. Actinomyces (IVW, β = 0.26, p = 0.0083), Butyricimonas (IVW, β = 0.21, p = 0.0184), and Lachnospiraceae (FCS020 group) (IVW, β = 0.24, p = 0.0194) were suggestive of causal associations with Phenoage acceleration. This Mendelian randomization study found that Streptococcus was causally associated with Bioage acceleration. Further randomized controlled trials are needed to investigate its role in the aging process.

Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation

Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.

The role of blood metabolites in oral cancer: insights from a Mendelian randomization approach

Aim

This research aimed to explore the causal impact of blood metabolites on oral cancer using a two-sample Mendelian randomization (MR) analysis. The study endeavored to identify potential biomarkers for oral cancer's clinical management.

Materials and methods

Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) of 486 human blood metabolites and evaluated the effect on oral cancer. Various statistical methods, including inverse variance weighted (IVW), MR-Egger, and weighted median, among others, were employed to analyze the potential causal relationship between blood metabolites and oral cancer. The sensitivity analyses were conducted using Cochran's Q tests, funnel plots, leave-one-out analyses, and MR-Egger intercept tests.

Results

29 metabolites met the stringent selection criteria. Out of these, 14 metabolites demonstrated a positive association with oral cancer risk, while 15 metabolites indicated a protective effect against oral cancer. The IVW-derived estimates were significant, and the results were consistent across different statistical methodologies. Both the Cochran Q test and the MR-Egger intercept test indicated no heterogeneity and pleiotropy.

Conclusion

This MR study offers evidence of the role specific blood metabolites play in oral cancer, pinpointing several with potential risk or protective effects. These findings could be helpful for new diagnostic tools and treatments for oral cancer. While the results are promising, additional research is necessary to fully validate and refine these conclusions. This study serves as a foundational step towards more comprehensive understandings in the future.

Mendelian randomization analysis reveals an independent causal relationship between four gut microbes and acne vulgaris

Background

Numerous studies have suggested a correlation between gut microbiota and acne vulgaris; however, no specific causal link has been explored.

Materials and methods

To investigate the possible causal relationship between gut microbiota and acne vulgaris, this study employed a large-scale genome-wide association study (GWAS) summary statistic. Initially, a two-sample Mendelian randomization (MR) analysis was utilized to identify the specific gut microflora responsible for acne vulgaris. We used the Inverse Variance Weighted (IVW) method as the main MR analysis method. Additionally, we assessed heterogeneity and horizontal pleiotropy, while also examining the potential influence of individual single-nucleotide polymorphisms (SNPs) on the analysis results. In order to eliminate gut microbiota with reverse causal associations, we conducted reverse MR analysis. Multivariate Mendelian randomization analysis (MVMR) was then employed to verify the independence of the causal associations. Finally, we performed SNP annotation on the instrumental variables of independent gut microbiota and acne vulgaris to determine the genes where these genetic variations are located. We also explored the biological functions of these genes through enrichment analysis.

Result

The IVW method of forward MR identified nine gut microbes with a causal relationship with acne vulgaris (p < 0.05). The findings from the sensitivity analysis demonstrate the absence of heterogeneity or horizontal pleiotropy, and leave-one-out analysis indicates that the results are not driven by a single SNP. Additionally, the Reverse MR analysis excluded two reverse-correlated pathogenic gut microbes. And then, MVMR was used to analyze seven gut microbes, and it was found that Cyanobacterium and Family XIII were risk factors for acne vulgaris, while Ruminococcus1 and Ruminiclostridium5 were protective factors for acne vulgaris. After conducting biological annotation, we identified six genes (PLA2G4A, FADS2, TIMP17, ADAMTS9, ZC3H3, and CPSF4L) that may be associated with the pathogenic gut microbiota of acne vulgaris patients. The enrichment analysis results indicate that PLA2G4A/FADS2 is associated with fatty acid metabolism pathways.

Conclusion

Our study found independent causal relationships between four gut microbes and acne vulgaris, and revealed a genetic association between acne vulgaris patients and gut microbiota. Consider preventing and treating acne vulgaris by interfering with the relative content of these four gut microbes.

Ginsenoside Rk3 modulates gut microbiota and regulates immune response of group 3 innate lymphoid cells to against colorectal tumorigenesis

The gut microbiota plays a pivotal role in the immunomodulatory and protumorigenic microenvironment of colorectal cancer (CRC). However, the effect of ginsenoside Rk3 (Rk3) on CRC and gut microbiota remains unclear. Therefore, the purpose of this study is to explore the potential effect of Rk3 on CRC from the perspective of gut microbiota and immune regulation. Our results reveal that treatment with Rk3 significantly suppresses the formation of colon tumors, repairs intestinal barrier damage, and regulates the gut microbiota imbalance caused by CRC, including enrichment of probiotics such as Akkermansia muciniphila and Barnesiella intestinihominis, and clearance of pathogenic Desulfovibrio. Subsequent metabolomics data demonstrate that Rk3 can modulate the metabolism of amino acids and bile acids, particularly by upregulating glutamine, which has the potential to regulate the immune response. Furthermore, we elucidate the regulatory effects of Rk3 on chemokines and inflammatory factors associated with group 3 innate lymphoid cells (ILC3s) and T helper 17 (Th17) signaling pathways, which inhibits the hyperactivation of the Janus kinase-signal transducer and activator of transcription 3 (JAK-STAT3) signaling pathway. These results indicate that Rk3 modulates gut microbiota, regulates ILC3s immune response, and inhibits the JAK-STAT3 signaling pathway to suppress the development of colon tumors. More importantly, the results of fecal microbiota transplantation suggest that the inhibitory effect of Rk3 on colon tumors and its regulation of ILC3 immune responses are mediated by the gut microbiota. In summary, these findings emphasize that Rk3 can be utilized as a regulator of the gut microbiota for the prevention and treatment of CRC.

A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome

The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.

Effects of bathing in different hot spring types on Japanese gut microbiota

Hot springs have been used for a variety of purposes, including the treatment and amelioration of illness and recreation. Japan has ten different types of therapeutic springs (described here as spa types), which are traditionally believed to have different efficacy. However, more research must be conducted to determine how they affect healthy people. Therefore, this study focused on the gut microbiota and aimed to investigate changes in the gut microbiota in healthy people after bathing in different spa types. Using Beppu's hot springs (simple, chloride, bicarbonate, sulfur, and sulfate types), 136 healthy Japanese adults living in the Kyushu area participated in the study and bathed in the same hot spring for seven days. Fecal samples were collected before and after the 7-day bathing period, and the relative abundance of the gut microbiota was determined by 16S rRNA sequencing. The results showed that the relative abundance of Bifidobacterium bifidum increased significantly after seven consecutive days of bathing in the bicarbonate spring. Significant increases in other gut microbiota were also observed after bathing in simple, bicarbonate, and sulfur springs. These results suggest that bathing in different hot springs may affect the gut microbiota in healthy individuals differently.

Gut microbiota, circulating metabolites, and gallstone disease: a Mendelian randomization study

Background

The link between Gut microbiota (GM) and Gallstone disease (GSD) is well established, but it is not clear whether there is a causal relationship between the two associations.

Methods

We conducted bidirectional Mendelian randomization (MR) analyses, leveraging aggregated data from the Genome-Wide Association Study (GWAS) of GM and Circulating Metabolites. Our primary objective was to investigate the causal interplay between intestinal flora and GSD. Additionally, we performed mediational analyses, two-step MR, and multivariate MR to uncover the potential mediating effect of circulating metabolites in this relationship.

Result

Our study has revealed a causal relationship between GSD and six distinct bacterial groups. Genetically predicted Class Bacilli (Odds Ratio (OR): 0.901, 95% Confidence Interval (95% CI): 0.825-0.985; p = 0.021), Order Lactobacillales (OR: 0.895, 95% CI: 0.816-0.981; p = 0.017), and Genus Coprococcus 2 (OR: 0.884, 95% CI: 0.804-0.973; p = 0.011) were inversely associated with the risk of GSD. Conversely, the Genus Clostridiumsensustricto1 (OR: 1.158, 95% CI: 1.029-1.303; p = 0.015), Genus Coprococcus3 (OR: 1.166, 95% CI: 1.024-1.327; p = 0.020), and Genus Peptococcus (OR: 1.070, 95% CI: 1.017-1.125; p = 0.009) were positively associated with the risk of GSD. Moreover, our findings suggest that the positive influence of the Genus Peptococcus on GSD may be mediated through Omega-3 polyunsaturated fatty acids (PUFA).

Conclusion

This study reinforces the connection between the gut microbiome and the risk of GSD while also unveiling the mediating role of Omega-3 PUFA in the causal relationship between these factors.

Causal relationship between gut microbiota and gastrointestinal diseases: a mendelian randomization study

BACKGROUND

Recent research increasingly highlights a strong correlation between gut microbiota and the risk of gastrointestinal diseases. However, whether this relationship is causal or merely coincidental remains uncertain. To address this, a Mendelian randomization (MR) analysis was undertaken to explore the connections between gut microbiota and prevalent gastrointestinal diseases.

METHODS

Genome-wide association study (GWAS) summary statistics for gut microbiota, encompassing a diverse range of 211 taxa (131 genera, 35 families, 20 orders, 16 classes, and 9 phyla), were sourced from the comprehensive MiBioGen study. Genetic associations with 22 gastrointestinal diseases were gathered from the UK Biobank, FinnGen study, and various extensive GWAS studies. MR analysis was meticulously conducted to assess the causal relationship between genetically predicted gut microbiota and these gastrointestinal diseases. To validate the reliability of our findings, sensitivity analyses and tests for heterogeneity were systematically performed.

RESULTS

The MR analysis yielded significant evidence for 251 causal relationships between genetically predicted gut microbiota and the risk of gastrointestinal diseases. This included 98 associations with upper gastrointestinal diseases, 81 with lower gastrointestinal diseases, 54 with hepatobiliary diseases, and 18 with pancreatic diseases. Notably, these associations were particularly evident in taxa belonging to the genera Ruminococcus and Eubacterium. Further sensitivity analyses reinforced the robustness of these results.

CONCLUSIONS

The findings of this study indicate a potential genetic predisposition linking gut microbiota to gastrointestinal diseases. These insights pave the way for designing future clinical trials focusing on microbiome-related interventions, including the use of microbiome-dependent metabolites, to potentially treat or manage gastrointestinal diseases and their associated risk factors.

Putative causal relations among gut flora, serums metabolites and arrhythmia: a Mendelian randomization study

BACKGROUND

The pathogenesis of cardiac arrhythmias is multifaceted, encompassing genetic, environmental, hemodynamic, and various causative factors. Emerging evidence underscores a plausible connection between gut flora, serum metabolites, and specific types of arrhythmias. Recognizing the role of host genetics in shaping the microbiota, we employed two-sample Mendelian randomization analyses to investigate potential causal associations between gut flora, serum metabolites, and distinct arrhythmias.

METHODS

Mendelian randomization methods were deployed to ascertain causal relationships between 211 gut flora, 575 serum metabolites, and various types of arrhythmias. To ensure the reliability of the findings, five complementary Mendelian randomization methods, including inverse variance weighting methods, were employed. The robustness of the results was scrutinized through a battery of sensitivity analyses, incorporating the Cochran Q test, leave-one-out test, and MR-Egger intercept analysis.

RESULTS

Eighteen gut flora and twenty-six serum metabolites demonstrated associations with the risk of developing atrial fibrillation. Moreover, ten gut flora and fifty-two serum metabolites were linked to the risk of developing supraventricular tachycardia, while eight gut flora and twenty-five serum metabolites were associated with the risk of developing tachycardia. Additionally, six gut flora and twenty-one serum metabolites exhibited associations with the risk of developing bradycardia.

CONCLUSION

This study revealed the potential causal relationship that may exist between gut flora, serum metabolites and different cardiac arrhythmias and highlights the need for further exploration. This study provides new perspectives to enhance diagnostic and therapeutic strategies in the field of cardiac arrhythmias.

The therapeutic potential of dietary intervention: based on the mechanism of a tryptophan derivative-indole propionic acid on metabolic disorders

Tryptophan (TRP) contributes to individual immune homeostasis and good condition via three complex metabolism pathways (5-hydroxytryptamine (5-HT), kynurenine (KP), and gut microbiota pathway). Indole propionic acid (IPA), one of the TRP derivatives of the microbiota pathway, has raised more attention because of its impact on metabolic disorders. Here, we retrospect increasing evidence that TRP metabolites/IPA derived from its proteolysis impact host health and disease. IPA can activate the immune system through aryl hydrocarbon receptor (AHR) and/or Pregnane X receptor (PXR) as a vital mediator among diet-caused host and microbe cross-talk. Different levels of IPA in systemic circulation can predict the risk of NAFLD, T2DM, and CVD. IPA is suggested to alleviate cognitive impairment from oxidative damage, reduce gut inflammation, inhibit lipid accumulation and attenuate the symptoms of NAFLD, putatively enhance the intestinal epithelial barrier, and maintain intestinal homeostasis. Now, we provide a general description of the relationships between IPA and various physiological and pathological processes, which support an opportunity for diet intervention for metabolic diseases.

Meta-analysis identifying gut microbial biomarkers of Qinghai-Tibet Plateau populations and the functionality of microbiota-derived butyrate in high-altitude adaptation

The extreme environmental conditions of a plateau seriously threaten human health. The relationship between gut microbiota and human health at high altitudes has been extensively investigated. However, no universal gut microbiota biomarkers have been identified in the plateau population, limiting research into gut microbiota and high-altitude adaptation. 668 16s rRNA samples were analyzed using meta-analysis to reduce batch effects and uncover microbiota biomarkers in the plateau population. Furthermore, the robustness of these biomarkers was validated. Mendelian randomization (MR) results indicated that Tibetan gut microbiota may mediate a reduced erythropoietic response. Functional analysis and qPCR revealed that butyrate may be a functional metabolite in high-altitude adaptation. A high-altitude rat model showed that butyrate reduced intestinal damage caused by high altitudes. According to cell experiments, butyrate may downregulate hypoxia-inducible factor-1α (HIF-1α) expression and blunt cellular responses to hypoxic stress. Our research found universally applicable biomarkers and investigated their potential roles in promoting human health at high altitudes.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Role of polygenic risk scores in the association between chronotype and health risk behaviors

BACKGROUND

This study explores the association between chronotypes and adolescent health risk behaviors (HRBs) by testing how genetic background moderates these associations and clarifies the influence of chronotypes and polygenic risk score (PRS) on adolescent HRBs.

METHODS

Using VOS-viewer software to select the corresponding data, this study used knowledge domain mapping to identify and develop the research direction with respect to adolescent risk factor type. Next, DNA samples from 264 students were collected for low-depth whole-genome sequencing. The sequencing detected HRB risk loci, 49 single nucleotide polymorphisms based to significant SNP. Subsequently, PRSs were assessed and divided into low, moderate, and high genetic risk according to the tertiles and chronotypes and interaction models were constructed to evaluate the association of interaction effect and clustering of adolescent HRBs. The chronotypes and the association between CLOCK-PRS and HRBs were examined to explore the association between chronotypes and mental health and circadian CLOCK-PRS and HRBs.

RESULTS

Four prominent areas were displayed by clustering information fields in network and density visualization modes in VOS-viewer. The total score of evening chronotypes correlated with high-level clustering of HRBs in adolescents, co-occurrence, and mental health, and the difference was statistically significant. After controlling covariates, the results remained consistent. Three-way interactions between chronotype, age, and mental health were observed, and the differences were statistically significant. CLOCK-PRS was constructed to identify genetic susceptibility to the clustering of HRBs. The interaction of evening chronotypes and high genetic risk CLOCK-PRS was positively correlated with high-level clustering of HRBs and HRB co-occurrence in adolescents, and the difference was statistically significant. The interaction between the sub-dimensions of evening chronotypes and the high genetic CLOCK-PRS risk correlated with the outcome of the clustering of HRBs and HRB co-occurrence.

CONCLUSIONS

The interaction of PRS and chronotype and the HRBs in adolescents appear to have an association, and the three-way interaction between the CLOCK-PRS, chronotype, and mental health plays important roles for HRBs in adolescents.

Specific gut microbiota may increase the risk of erectile dysfunction: a two-sample Mendelian randomization study

Objective

Studies have found that gut microbiota may be associated with the development of erectile dysfunction (ED); however, the exact link between the two remains unclear. This study aimed to elucidate the relationship between the gut microbiota and the risk of ED from a genetic perspective.

Methods

We investigated the relationship between the gut microflora and ED using two-sample Mendelian randomization. GWAS-pooled data for ED were obtained from 223805 participants in Europe. GWAS summary data for ED were obtained from 223805 subjects in Europe and that for the gut microbiota were obtained from 18340 participants in 24 cohorts. We used the inverse-variance weighted (IVW) estimator as the primary method for the preliminary analysis, and the MR-Egger, weighted median (WM), simple model, and weighted model as secondary methods. We used Cochrane's Q-test, to detect heterogeneity, MREgger to detect pleiotropy, and the leave-one-out method to test the stability of the MR results. Ultimately, we genetically predicted a causal relationship between 211 gut microbiota and ED.

Results

A total of 2818 SNPs associated with gut microflora were screened in the ED correlation analysis based on the assumption of instrumental variables. The results of MR analysis showed a causal relationship between the six gut microbes and ED occurrence. The results of the fixed effects IVW method revealed five gut microflora, including Lachnospiraceae (OR, 1.265; P = 0.008), Lachnospiraceae NC2004 group (OR, 1.188; P = 0.019), Oscillibacter (OR, 1.200; P = 0.015), Senegalimassilia (OR, 1.355; P = 0.002), Tyzzerella3 (OR, 1.133; P = 0.022), to be negatively associated with ED. In addition, the IVW method revealed Ruminococcaceae UCG-013 (OR, 0.827; P = 0.049) to be positively associated with ED. Quality control results showed no heterogeneity or horizontal pleiotropy in the MR analysis (P > 0.05).

Conclusions

Six gut microbes were genetically associated with ED; of which, Ruminococcaceae UCG-013 was causally associated with a reduced risk of ED development. Our findings provide a new direction for research on the prevention and treatment of ED; however, the mechanisms and details require further investigation.

Gut microbiome and frailty: insight from genetic correlation and mendelian randomization

Observational studies have shown that the gut microbiome is associated with frailty. However, whether these associations underlie causal effects remains unknown. Thus, this study aimed to assess the genetic correlation and causal relationships between the genetically predicted gut microbiome and frailty using linkage disequilibrium score regression (LDSC) and Mendelian Randomization (MR). Summary statistics for the gut microbiome were obtained from a genome-wide association study (GWAS) meta-analysis of the MiBioGen consortium (N = 18,340). Summary statistics for frailty were obtained from a GWAS meta-analysis, including the UK Biobank and TwinGene (N = 175,226). We used LDSC and MR analyses to estimate the genetic correlation and causality between the genetically predicted gut microbiome and frailty. Our findings indicate a suggestive genetic correlation between Christensenellaceae R-7 and frailty. Moreover, we found evidence for suggestive causal effects of twelve genus-level gut microbes on frailty using at least two MR methods. There was no evidence of horizontal pleiotropy or heterogeneity in the MR analysis. This study provides suggestive evidence for a potential genetic correlation and causal association between several genetically predicted gut microbes and frailty. More population-based observational studies and animal experiments are required to clarify this association and the underlying mechanisms.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Analysis of gut microbiome, host genetics, and plasma metabolites reveals gut microbiome-host interactions in the Japanese population

Interaction between the gut microbiome and host plays a key role in human health. Here, we perform a metagenome shotgun-sequencing-based analysis of Japanese participants to reveal associations between the gut microbiome, host genetics, and plasma metabolome. A genome-wide association study (GWAS) for microbial species (n = 524) identifies associations between the PDE1C gene locus and Bacteroides intestinalis and between TGIF2 and TGIF2-RAB5IF gene loci and Bacteroides acidifiaciens. In a microbial gene ortholog GWAS, agaE and agaS, which are related to the metabolism of carbohydrates forming the blood group A antigen, are associated with blood group A in a manner depending on the secretor status determined by the East Asian-specific FUT2 variant. A microbiome-metabolome association analysis (n = 261) identifies associations between bile acids and microbial features such as bile acid metabolism gene orthologs including bai and 7β-hydroxysteroid dehydrogenase. Our publicly available data will be a useful resource for understanding gut microbiome-host interactions in an underrepresented population.

Distinct Microbial Taxa Are Associated with LDL-Cholesterol Reduction after 12 Weeks of Lactobacillus plantarum Intake in Mild Hypercholesterolemia: Results of a Randomized Controlled Study

Probiotic microbes such as Lactobacillus may reduce serum total cholesterol (TC) and low-density lipoprotein (LDL) cholesterol. The objective of this study was to assess the effect of Lactobacillus plantarum strains CECT7527, CECT7528, and CECT7529 (LP) on the serum lipids, cardiovascular parameters, and fecal gut microbiota composition in patients with mild hypercholesterolemia. A randomized, double-blinded, placebo-controlled clinical trial with 86 healthy adult participants with untreated elevated LDL cholesterol ≥ 160 mg/dl was conducted. Participants were randomly allocated to either placebo or LP (1.2 × 109 CFU/d) for 12 weeks. LDL, HDL, TC, and triglycerides (TG), cardiovascular parameters (blood pressure, arterial stiffness), and fecal gut microbiota composition (16S rRNA gene sequencing) were assessed at baseline and after 12 weeks. Both groups were comparable regarding age, sex, and LDL-C at baseline. LDL-C decreased (mean decrease - 6.6 mg/dl ±  - 14.0 mg/dl, Ptime*group = 0.006) in the LP group but not in the placebo group. No effects were observed on HDL, TG, or cardiovascular parameters or overall gut microbiota composition. Responders to LP intervention (> 5% LDL-C reduction) were characterized by higher BMI, pronounced TC reduction, higher abundance of fecal Roseburia, and lower abundance of Oscillibacter. In conclusion, 12 weeks of L. plantarum intake moderately reduced LDL-C and TC as compared to placebo. LDL-C-lowering efficacy of L. plantarum strains may potentially be dependent on individual difference in the gut microbiota. Trial registration: DRKS00020384, dated 07/01/2020.

Roles of gut microbiota in atrial fibrillation: insights from Mendelian randomization analysis and genetic data from over 430,000 cohort study participants

BACKGROUND

Gut microbiota imbalances have been suggested as a contributing factor to atrial fibrillation (AF), but the causal relationship is not fully understood.

OBJECTIVES

To explore the causal relationships between the gut microbiota and AF using Mendelian randomization (MR) analysis.

METHODS

Summary statistics were from genome-wide association studies (GWAS) of 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) (the Dutch Microbiome Project) and two large meta-GWASs of AF. The significant results were validated in FinnGen cohort and over 430,000 UK Biobank participants. Mediation MR analyses were conducted for AF risk factors, including type 2 diabetes, coronary artery disease (CAD), body mass index (BMI), blood lipids, blood pressure, and obstructive sleep apnea, to explore the potential mediation effect of these risk factors in between the gut microbiota and AF.

RESULTS

Two microbial taxa causally associated with AF: species Eubacterium ramulus (odds ratio [OR] 1.08, 95% confidence interval [CI] 1.04-1.12, P = 0.0001, false discovery rate (FDR) adjusted p-value = 0.023) and genus Holdemania (OR 1.15, 95% CI 1.07-1.25, P = 0.0004, FDR adjusted p-value = 0.042). Genus Holdemania was associated with incident AF risk in the UK Biobank. The proportion of mediation effect of species Eubacterium ramulus via CAD was 8.05% (95% CI 1.73% - 14.95%, P = 0.008), while the proportion of genus Holdemania on AF via BMI was 12.01% (95% CI 5.17% - 19.39%, P = 0.0005).

CONCLUSIONS

This study provided genetic evidence to support a potential causal mechanism between gut microbiota and AF and suggested the mediation role of AF risk factors.

Association of nicotine dependence and gut microbiota: a bidirectional two-sample Mendelian randomization study

Background

Nicotine dependence is a key factor influencing the diversity of gut microbiota, and targeting gut microbiota may become a new approach for the prevention and treatment of nicotine dependence. However, the causal relationship between the two is still unclear. This study aims to investigate the causal relationship between nicotine dependence and gut microbiota.

Methods

A two-sample bidirectional Mendelian randomization (MR) study was conducted using the largest existing gut microbiota and nicotine dependence genome-wide association studies (GWAS). Causal relationships between genetically predicted nicotine dependence and gut microbiota abundance were examined using inverse variance weighted, MR-Egger, weighted median, simple mode, weighted mode, and MR-PRESSO approaches. Cochrane's Q test, MR-Egger intercept test, and leave-one-out analysis were performed as sensitivity analyses to assess the robustness of the results. Multivariable Mendelian randomization analysis was also conducted to eliminate the interference of smoking-related phenotypes. Reverse Mendelian randomization analysis was then performed to determine the causal relationship between genetically predicted gut microbiota abundance and nicotine dependence.

Results

Genetically predicted nicotine dependence had a causal effect on Christensenellaceae (β: -0.52, 95% CI: -0.934-0.106, P = 0.014). The Eubacterium xylanophilum group (OR: 1.106, 95% CI: 1.004-1.218), Lachnoclostridium (OR: 1.118, 95% CI: 1.001-1.249) and Holdemania (OR: 1.08, 95% CI: 1.001-1.167) were risk factors for nicotine dependence. Peptostreptococcaceae (OR: 0.905, 95% CI: 0.837-0.977), Desulfovibrio (OR: 0.014, 95% CI: 0.819-0.977), Dorea (OR: 0.841, 95% CI. 0.731-0.968), Faecalibacterium (OR: 0.831, 95% CI: 0.735-0.939) and Sutterella (OR: 0.838, 95% CI: 0.739-0.951) were protective factor for nicotine dependence. The sensitivity analysis showed consistent results.

Conclusion

The Mendelian randomization study confirmed the causal link between genetically predicted risk of nicotine dependence and genetically predicted abundance of gut microbiota. Gut microbiota may serve as a biomarker and offer insights for addressing nicotine dependence.

Convergent application of traditional Chinese medicine and gut microbiota in ameliorate of cirrhosis: a data mining and Mendelian randomization study

Objective

Traditional Chinese medicine (TCM) has been used for the treatment of chronic liver diseases for a long time, with proven safety and efficacy in clinical settings. Previous studies suggest that the therapeutic mechanism of TCM for hepatitis B cirrhosis may involve the gut microbiota. Nevertheless, the causal relationship between the gut microbiota, which is closely linked to TCM, and cirrhosis remains unknown. This study aims to utilize two-sample Mendelian randomization (MR) to investigate the potential causal relationship between gut microbes and cirrhosis, as well as to elucidate the synergistic mechanisms between botanical drugs and microbiota in treating cirrhosis.

Methods

Eight databases were systematically searched through May 2022 to identify clinical studies on TCM for hepatitis B cirrhosis. We analyzed the frequency, properties, flavors, and meridians of Chinese medicinals based on TCM theories and utilized the Apriori algorithm to identify the core botanical drugs for cirrhosis treatment. Cross-database comparison elucidated gut microbes sharing therapeutic targets with these core botanical drugs. MR analysis assessed consistency between gut microbiota causally implicated in cirrhosis and microbiota sharing therapeutic targets with key botanicals.

Results

Our findings revealed differences between the Chinese medicinals used for compensated and decompensated cirrhosis, with distinct frequency, dosage, properties, flavors, and meridian based on TCM theory. Angelicae Sinensis Radix, Salviae Miltiorrhizae Radix Et Rhizoma, Poria, Paeoniae Radix Alba, Astragali Radix, Atrctylodis Macrocephalae Rhizoma were the main botanicals. Botanical drugs and gut microbiota target MAPK1, VEGFA, STAT3, AKT1, RELA, JUN, and ESR1 in the treatment of hepatitis B cirrhosis, and their combined use has shown promise for cirrhosis treatment. MR analysis demonstrated a positive correlation between increased ClostridialesvadinBB60 and Ruminococcustorques abundance and heightened cirrhosis risk. In contrast, Eubacteriumruminantium, Lachnospiraceae, Eubacteriumnodatum, RuminococcaceaeNK4A214, Veillonella, and RuminococcaceaeUCG002 associated with reduced cirrhosis risk. Notably, Lachnospiraceae shares key therapeutic targets with core botanicals, which can treat cirrhosis at a causal level.

Conclusion

We identified 6 core botanical drugs for managing compensated and decompensated hepatitis B cirrhosis, despite slight prescription differences. The core botanical drugs affected cirrhosis through multiple targets and pathways. The shared biological effects between botanicals and protective gut microbiota offer a potential explanation for the therapeutic benefits of these key herbal components in treating cirrhosis. Elucidating these mechanisms provides crucial insights to inform new drug development and optimize clinical therapy for hepatitis B cirrhosis.